[1]吴一全,王凯,曹鹏祥.蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J].智能系统学报,2015,10(01):103-112.[doi:10.3969/j.issn.1673-4785.201403040]
 WU Yiquan,WANG Kai,CAO Pengxiang.Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization[J].CAAI Transactions on Intelligent Systems,2015,10(01):103-112.[doi:10.3969/j.issn.1673-4785.201403040]
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蜂群优化的二维非对称Tsallis交叉熵图像阈值选取(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年01期
页码:
103-112
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization
作者:
吴一全123 王凯1 曹鹏祥1
1. 南京航空航天大学 电子信息工程学院, 江苏 南京 210016;
2. 南京财经大学 江苏省粮油品质控制及深加工技术重点实验室, 江苏 南京 210046;
3. 南京林业大学 江苏省制浆造纸科学技术重点实验室, 江苏 南京 210037
Author(s):
WU Yiquan123 WANG Kai1 CAO Pengxiang1
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Jiangsu Key Laboratory of Quality Control and Further Processing of Cereals and Oils, Nanjing University of Finance Economics, Nanjing 210046, China;
3. Jiangsu Provincial Key Laboratory of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing 210037, China
关键词:
图像分割阈值选取二维Tsallis交叉熵递推算法蜂群优化区域间对比度
Keywords:
image segmentationthreshold selectiontwo-dimensionTsallis cross entropyrecursive algorithmsbee colony optimizationinter-regional contrast
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201403040
文献标志码:
A
摘要:
交叉熵能够度量图像分割前后的差异,与Shannon交叉熵相比,引入参数q的Tsallis交叉熵则为图像阈值分割提供了灵活性和普适性,而非对称Tsallis交叉熵的表达形式更加简洁。由此,提出了蜂群优化的二维非对称Tsallis交叉熵图像阈值选取方法。首先引出了非对称Tsallis交叉熵,导出了二维非对称Tsallis交叉熵阈值选取公式,并利用递推方式计算阈值选取准则函数涉及的中间变量,建立查找表,消除冗余运算;然后采用蜂群算法搜寻最佳二维阈值。大量实验结果表明,相对二维最大Shannon熵法、二维Shannon交叉熵法、二维Tsallis熵法和二维对称Tsallis交叉熵法等同类方法,所提出方法在主观视觉效果和区域间对比度评价指标上有较大的改善,能够更准确地分割出目标,运行速度也更快。
Abstract:
Cross entropy can measure the difference between the original image and its segmentation result. Compared with Shannon cross entropy, Tsallis cross entropy, in which a parameter q is introduced, provides flexibility and universality for the segmentation of image threshold. The asymmetric Tsallis cross entropy has more concise expression form. Therefore, a method of threshold selection is proposed based on the two-dimensional asymmetric Tsallis cross entropy using bee colony optimization. Firstly, the asymmetric Tsallis cross entropy is introduced and the threshold selection formulae based on the two-dimensional asymmetric Tsallis cross entropy are derived. Recursive algorithms are used to calculate the intermediate variables involved in criterion function for threshold selection and a lookup table is built to eliminate the redundant operations. The optimal two-dimensional threshold is searched by the bee colony algorithm. A large number of experiment results showed that the proposed method is greatly improved in terms of subjective visual effect and inter-regional contrast evaluation indicators compared to the relevant methods, such as the two-dimensional maximum Shannon entropy method, the two-dimensional Shannon cross entropy method, the two-dimensional Tsallis entropy method, and the two-dimensional symmetrical Tsallis cross entropy method. It can segment objects more accurately and has a faster running speed.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-3-14;改回日期:。
基金项目:国家自然科学基金资助项目(60872065);江苏省粮油品质控制及深加工技术重点实验室开放基金资助项目(LYPK201304);江苏省制浆造纸科学技术重点实验室开放基金资助项目(201313).
作者简介:吴一全,男,1963年生,教授,博士生导师,博士。主要研究方向为图像处理与分析、目标检测与识别、智能信息处理。发表学术论文230余篇,被引用1700余次;王凯,男,1988年生,硕士研究生,主要研究方向为图像处理与视频通信;曹鹏祥,男,1981年生,硕士研究生,主要研究方向为图像处理与视频通信。
通讯作者:吴一全.E-mail:nuaaimage@163.com.
更新日期/Last Update: 2015-06-16